• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.31-39
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• 2019

A robot usually adopts ANN (artificial neural network)-based object detection and instance segmentation algorithms to recognize objects but creating datasets for these algorithms requires high labeling costs because the dataset should be manually labeled. In order to lower the labeling cost, a new scheme is proposed that can automatically generate a training images and label them for specific objects. This scheme uses an instance segmentation algorithm trained to give the masks of unknown objects, so that they can be obtained in a simple environment. The RGB images of objects can be obtained by using these masks, and it is necessary to label the classes of objects through a human supervision. After obtaining object images, they are synthesized with various background images to create new images. Labeling the synthesized images is performed automatically using the masks and previously input object classes. In addition, human intervention is further reduced by using the robot arm to collect object images. The experiments show that the performance of instance segmentation trained through the proposed method is equivalent to that of the real dataset and that the time required to generate the dataset can be significantly reduced.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.780-789
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• 2018

Nowadays, gun crimes occur very frequently not only in public places but in alleyways around the world. In particular, it is essential to detect a person armed by a pistol to prevent those crimes since small guns, such as pistols, are often used for those crimes. Because conventional works for armed person detection have treated an armed person as a single object in an input image, their accuracy is very low. The reason for the low accuracy comes from the fact that the gunman is treated as a single object although the pistol is a relatively much smaller object than the person. To solve this problem, we propose a novel algorithm called APDA(Armed Person Detection Algorithm). APDA detects the armed person using in a post-processing the positions of both wrists and the pistol achieved by the CNN-based human body feature detection model and the pistol detection model, respectively. We show that APDA can provide both 46.3% better recall and 14.04% better precision than SSD-MobileNet.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3104-3120
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• 2017

The intelligent monitoring system has been successfully applied in many fields such as: monitoring of production lines, transportation, etc. Smart surveillance systems have been developed and proven effective in some specific areas such as monitoring of human activity, traffic, etc. Most of critical application monitoring systems involve object tracking as one of the key steps. However, task of tracking of moving object is not easy. In this paper, the authors propose a method to implement human object tracking in outdoor environment based on human features in shearlet domain. The proposed method uses shearlet transform which combines the human features with context-sensitiveness in order to improve the accuracy of human tracking. The proposed algorithm not only improves the edge accuracy, but also reduces wrong positions of the object between the frames. The authors validated the proposed method by calculating Euclidean distance and Mahalanobis distance values between centre of actual object and centre of tracked object, and it has been found that the proposed method gives better result than the other recent available methods.

• Journal of information and communication convergence engineering
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• v.18 no.4
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• pp.267-277
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• 2020

It is important to understand the exact habitat distribution of endangered species because of their decreasing numbers. In this study, we build a system with a deep learning module that collects the image data of endangered animals, processes the data, and saves the data automatically. The system provides a more efficient way than human effort for classifying images and addresses two problems faced in previous studies. First, specious answers were suggested in those studies because the probability distributions of answer candidates were calculated even if the actual answer did not exist within the group. Second, when there were more than two entities in an image, only a single entity was focused on. We applied an object detection algorithm (YOLO) to resolve these problems. Our system has an average precision of 86.79%, a mean recall rate of 93.23%, and a processing speed of 13 frames per second.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.1-12
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• 2016

Digital imaging technology has developed into a state-of-the-art IT convergence, composite industry beyond the limits of the multimedia industry, especially in the field of smart object recognition, face - Application developed various techniques have been actively studied in conjunction with the phone. Recently, face recognition technology through the object recognition technology and evolved into intelligent video detection recognition technology, image recognition technology object detection recognition process applies to skills through is applied to the IP camera, the image object recognition technology with face recognition and active research have. In this paper, we first propose the necessary technical elements of the human factor technology trends and look at the human object recognition based spFACS (Smile Progress Facial Action Coding System) for detecting smiles study plan of the image recognition technology recognizes objects. Study scheme 1). ASM algorithm. By suggesting ways to effectively evaluate psychological research skills through the image object 2). By applying the result via the face recognition object to the tooth area it is detected in accordance with the recognized facial expression recognition of a person demonstrated the effect of extracting the feature points.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.53-62
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• 2011

In this paper, high-tech IT Convergence, to the development of complex technology, special technology, video object recognition technology was considered only as a smart - phone technology with the development of personal portable terminal has been developed crossroads. Technology-based detection of 3D face recognition technology that recognizes objects detected through the intelligent video recognition technology has been evolving technologies based on image recognition, face detection technology with through the development speed is booming. In this paper, based on human face recognition technology to detect the object recognition image processing technology is applied through the face recognition technology applied to the IP camera is the party of the mouth, and allowed the ability to identify and apply the human face recognition, measurement techniques applied research is suggested. Study plan: 1) face model based face tracking technology was developed and applied 2) algorithm developed by PC-based measurement of human perception through the CPU load in the face value of their basic parameters can be tracked, and 3) bilateral distance and the angle of gaze can be tracked in real time, proved effective.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.237-240
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• 2001

An image processing and decision making method for the Automatic Surveillance System is proposed. The aim of our Automatic Surveillance System is to detect a moving object and make a decision on whether it is human or not. Various object features such as the ratio of the width and the length of the moving object, the distance dispersion between the principal axis and the object contour, the eigenvectors, the symmetric axes, and the areas if the segmented region are used in this paper. These features are not the unique and decisive characteristics for representing human Also, due to the outdoor image property, the object feature information is unavoidably vague and inaccurate. In order to make an efficient decision from the information, we use a fuzzy rules base system ai an approximate reasoning method. The fuzzy rules, combining various object features, are able to describe the conditions for making an intelligent decision. The fuzzy rule base system is initially constructed by heuristic approach and then, trained and tasted with input/output data Experimental result are shown, demonstrating the validity of our system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.1
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• pp.69-75
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• 2022

Due to the increase in the demand for anomaly detection according to the ageing of power generation facilities, the need for developing an anomaly detection method that can provide high-reliability turbine blade anomaly detection performance has been continuously raised. Additionally, the false detection results caused by a human error accelerates the increase of the need. In this paper, we propose an anomaly detection technique for turbine blades in power plants using deep neural networks. Experimental results prove that the proposed technique achieves stable anomaly detection performance while minimizing human factor intervention.

• Journal of the Korea Society of Computer and Information
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• v.16 no.9
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• pp.69-76
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• 2011

According to the related reports, about 60 percents of ship collisions have resulted from operating mistake caused by human factor. Specially, the report said that negligence of observation caused 66.8 percents of the accidents due to a human factor. Hence automatic detection and tracking of an object from an IR images are crucial for safety navigation because it can relieve officer's burden and remedies imperfections of human visual system. In this paper, we present a method to detect an object such as ship, rock and buoy from a sea IR image. Most edge directions of the sea image are horizontal and most vertical edges come out from the object areas. The presented method uses them as a characteristic for the object detection. Vertical edges are extracted from the input image and isolated edges are eliminated. Then morphological closing operation is performed on the vertical edges. This caused vertical edges that actually compose an object be connected and become an object candidate region. Next, reference object regions are extracted using horizontal edges, which appear on the boundaries between surface of the sea and the objects. Finally, object regions are acquired by sequentially integrating reference region and object candidate regions.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.839-842
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• 2009

It is used to differential image for moving object detection in general. But it is difficult to detect the accurate detection which uses differential image between frames. In this paper, the proposed method overcome the noise that is generated by camera, grabber card, or weather condition. It extract to moving big object such as human or vehicle. The proposed method process morphological filtering and binary for the image with noise, reduce error. We are expect to apply to a real-time moving object detection system at fog condition, pass the limit of the object detection method using the differential image.